CSIR-NEERI Unveils AI Tool to Track Vehicular Pollution

Post by : Avinab Raana

Photo : X / WIRED

India’s effort to tackle worsening urban air pollution has received a technological breakthrough as scientists at CSIR-NEERI have developed a cutting-edge artificial intelligence tool capable of mapping real-time vehicular pollution hotspots. The innovation is designed to help authorities better understand how road traffic contributes to air pollution and allow for faster, data-driven decision-making.

The system analyzes traffic data and converts it into real-time emission estimates, allowing city planners and environmental regulators to identify areas where vehicular pollution is most concentrated. As urban traffic continues to grow across Indian cities, such tools could become essential for managing air quality and protecting public health.

The new technology works through an AI-driven digital dashboard that processes live traffic footage captured through roadside cameras. Using advanced computer vision algorithms, the system identifies and categorizes vehicles moving along city roads, including two-wheelers, cars, buses, and heavy commercial vehicles.

Once vehicles are identified, the platform calculates the likely emissions produced by each category based on known pollution factors. This allows the system to estimate pollutants such as particulate matter, nitrogen oxides, and carbon monoxide generated by traffic in real time.

By transforming ordinary traffic camera feeds into powerful environmental data, the tool provides a new way to track and measure pollution levels across busy urban corridors.

One of the most important capabilities of the AI platform is its ability to map pollution hotspots instantly. The system integrates its emission calculations with geographic information systems to display pollution levels across specific roads, intersections, and traffic zones.

Authorities can monitor hourly traffic activity and observe how congestion directly influences emission levels. During peak traffic periods, the system can highlight areas where pollution intensifies, giving policymakers a clear picture of the locations that require urgent attention.

This real-time insight is particularly valuable because traditional pollution monitoring methods often rely on static air-quality stations that cannot pinpoint the exact sources of emissions.

Urban planners and environmental agencies have long faced challenges in identifying the precise impact of traffic on city air quality. Conventional emission studies require extensive surveys and data collection, often taking months to produce actionable insights.

The AI-powered system dramatically shortens this process by providing instant emission estimates based on real traffic conditions. With this information, authorities can implement targeted interventions such as adjusting traffic signals, rerouting vehicles, or strengthening emission regulations in high-pollution zones. Such technology-driven strategies could significantly improve how cities manage environmental risks linked to transportation.

The development of this AI tool represents a broader shift toward data-driven environmental governance. As cities grow larger and vehicle numbers continue to rise, advanced digital tools will become essential for balancing mobility needs with environmental sustainability.

By combining artificial intelligence, traffic analytics, and environmental science, the new platform demonstrates how innovation can transform pollution monitoring from a reactive process into a proactive system.

If widely adopted, the technology could help cities respond faster to pollution challenges and build smarter, cleaner urban environments where mobility and environmental protection move forward together.

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